tract_data/

tensor.rs

//! `Tensor`, tract main data object of interest.
use crate::TVec;
use crate::blob::Blob;
use crate::datum::{ClampCast, Datum, DatumType, QParams, round_ties_to_even, scale_by};
use crate::dim::TDim;
use crate::internal::*;
use crate::opaque::Opaque;
use half::f16;
use itertools::{Itertools, izip};
use ndarray::prelude::*;
#[cfg(feature = "complex")]
use num_complex::Complex;
use num_traits::{Float, Zero};
use std::borrow::Cow;
use std::fmt;
use std::hash::Hash;
use std::ops::Range;
use std::sync::Arc;

pub mod dense_view;
pub mod litteral;
pub mod storage;
pub mod view;

pub use dense_view::{DenseView, DenseViewMut};
use storage::{DenseStorage, StorageKind};

#[derive(Copy, Clone, Default, Debug)]
pub enum Approximation {
    Exact,
    #[default]
    Close,
    Approximate,
    VeryApproximate,
    SuperApproximate,
    UltraApproximate,
    Custom(f32, f32, f32),
}

impl PartialEq for Approximation {
    fn eq(&self, other: &Self) -> bool {
        use Approximation::Custom;
        if let (Custom(aa, ar, ao), Custom(ba, br, bo)) = (self, other) {
            aa == ba && ar == br && bo == ao
        } else {
            std::mem::discriminant(self) == std::mem::discriminant(other)
        }
    }
}

impl Eq for Approximation {}

impl From<bool> for Approximation {
    fn from(b: bool) -> Self {
        if b { Self::Approximate } else { Self::Exact }
    }
}

impl Approximation {
    fn atol_rtol_outliers(&self, dt: &DatumType) -> (f64, f64, f64) {
        use Approximation::*;
        match (self, dt) {
            (Exact, _) => (0.0, 0.0, 0.0),
            (Close, DatumType::F16) => (1e-3, 1e-3, 0.0),
            (Approximate, DatumType::F16) => (1e-3, 5e-3, 0.0),
            (Approximate, qp) if qp.is_quantized() => (qp.zp_scale().1 as f64, 0., 0.0),
            (Close, _) => (1e-7, 1e-7, 0.0),
            (Approximate, _) => (1e-4, 5e-4, 0.0),
            (VeryApproximate, _) => (5e-2, 1e-2, 0.0),
            (SuperApproximate, _) => (0.1, 0.05, 0.0001),
            (UltraApproximate, _) => (0.2, 0.1, 0.0005),
            (Custom(atol, rtol, out), _) => (*atol as _, *rtol as _, *out as _),
        }
    }
}

/// Tensor is a concrete tensor in tract.
pub struct Tensor {
    dt: DatumType,
    shape: TVec<usize>,
    strides: TVec<isize>,
    len: usize,
    storage: StorageKind,
}

unsafe impl Send for Tensor {}
unsafe impl Sync for Tensor {}

impl Hash for Tensor {
    fn hash<H: std::hash::Hasher>(&self, state: &mut H) {
        use DatumType::*;
        self.dt.hash(state);
        self.shape.hash(state);
        self.dense_storage().layout().align().hash(state);
        unsafe {
            match self.dt {
                Bool => self.as_slice_unchecked::<bool>().hash(state),
                I8 => self.as_slice_unchecked::<i8>().hash(state),
                I16 => self.as_slice_unchecked::<i16>().hash(state),
                I32 => self.as_slice_unchecked::<i32>().hash(state),
                I64 => self.as_slice_unchecked::<i64>().hash(state),
                U8 => self.as_slice_unchecked::<u8>().hash(state),
                U16 => self.as_slice_unchecked::<u16>().hash(state),
                U32 => self.as_slice_unchecked::<u32>().hash(state),
                U64 => self.as_slice_unchecked::<u64>().hash(state),
                F16 => self.as_slice_unchecked::<i16>().hash(state),
                F32 => self.as_slice_unchecked::<i32>().hash(state),
                F64 => self.as_slice_unchecked::<i64>().hash(state),
                TDim => self.as_slice_unchecked::<crate::dim::TDim>().hash(state),
                String => self.as_slice_unchecked::<std::string::String>().hash(state),
                Blob => self.as_slice_unchecked::<crate::blob::Blob>().hash(state),
                Opaque => self.as_slice_unchecked::<crate::opaque::Opaque>().hash(state),
                QI8(_) => self.as_slice_unchecked::<i8>().hash(state),
                QU8(_) => self.as_slice_unchecked::<u8>().hash(state),
                QI32(_) => self.as_slice_unchecked::<i32>().hash(state),
                #[cfg(feature = "complex")]
                ComplexI16 => self.as_slice_unchecked::<Complex<i16>>().hash(state),
                #[cfg(feature = "complex")]
                ComplexI32 => self.as_slice_unchecked::<Complex<i32>>().hash(state),
                #[cfg(feature = "complex")]
                ComplexI64 => self.as_slice_unchecked::<Complex<i64>>().hash(state),
                #[cfg(feature = "complex")]
                ComplexF16 => self.as_slice_unchecked::<Complex<i16>>().hash(state),
                #[cfg(feature = "complex")]
                ComplexF32 => self.as_slice_unchecked::<Complex<i32>>().hash(state),
                #[cfg(feature = "complex")]
                ComplexF64 => self.as_slice_unchecked::<Complex<i64>>().hash(state),
            }
        }
    }
}

impl Clone for Tensor {
    fn clone(&self) -> Tensor {
        self.deep_clone()
    }
}

impl Default for Tensor {
    fn default() -> Tensor {
        litteral::tensor0(0f32)
    }
}

impl Drop for Tensor {
    fn drop(&mut self) {
        macro_rules! drop_in_place {
            ($t: ty) => {
                if self.dt == <$t>::datum_type() {
                    unsafe {
                        let slice = self.as_slice_mut_unchecked::<$t>();
                        std::ptr::drop_in_place(slice as *mut [$t]);
                    }
                }
            };
        }
        drop_in_place!(Blob);
        drop_in_place!(String);
        drop_in_place!(TDim);
        drop_in_place!(Opaque);
    }
}

#[allow(unreachable_code)]
pub fn vector_size() -> usize {
    #[cfg(target_arch = "x86_64")]
    {
        return if is_x86_feature_detected!("avx512f") { 512 / 8 } else { 256 / 8 };
    }
    128 / 8
}

impl Tensor {
    #[inline]
    fn dense_storage(&self) -> &DenseStorage {
        self.storage.as_dense().expect("Non-dense storage")
    }

    #[inline]
    fn dense_storage_mut(&mut self) -> &mut DenseStorage {
        self.storage.as_dense_mut().expect("Non-dense storage")
    }

    /// Returns an immutable [`DenseView`] if this tensor has dense storage.
    #[inline]
    pub fn as_dense(&self) -> Option<DenseView<'_>> {
        let storage = self.storage.as_dense()?;
        Some(DenseView::new(self, storage))
    }

    /// Returns an immutable [`DenseView`], or an error if storage is not dense.
    #[inline]
    pub fn try_as_dense(&self) -> TractResult<DenseView<'_>> {
        self.as_dense().context("Tensor storage is not dense")
    }

    /// Returns a mutable [`DenseViewMut`] if this tensor has dense storage.
    #[inline]
    pub fn as_dense_mut(&mut self) -> Option<DenseViewMut<'_>> {
        let storage = self.storage.as_dense_mut()?;
        Some(DenseViewMut::new(self.dt, &self.shape, &self.strides, self.len, storage))
    }

    /// Returns a mutable [`DenseViewMut`], or an error if storage is not dense.
    #[inline]
    pub fn try_as_dense_mut(&mut self) -> TractResult<DenseViewMut<'_>> {
        self.as_dense_mut().context("Tensor storage is not dense")
    }

    /// Create an uninitialized tensor (dt as type paramater).
    #[inline]
    pub unsafe fn uninitialized<T: Datum>(shape: &[usize]) -> TractResult<Tensor> {
        unsafe { Self::uninitialized_dt(T::datum_type(), shape) }
    }

    /// Create an uninitialized tensor (dt as regular parameter).
    #[inline]
    pub unsafe fn uninitialized_dt(dt: DatumType, shape: &[usize]) -> TractResult<Tensor> {
        unsafe { Self::uninitialized_aligned_dt(dt, shape, vector_size()) }
    }

    /// Create an uninitialized tensor with a given alignment (in bytes).
    #[inline]
    pub unsafe fn uninitialized_aligned<T: Datum>(
        shape: &[usize],
        alignment: usize,
    ) -> TractResult<Tensor> {
        unsafe { Self::uninitialized_aligned_dt(T::datum_type(), shape, alignment) }
    }

    /// Create an uninitialized tensor with a given alignment (in bytes).
    pub unsafe fn uninitialized_aligned_dt(
        dt: DatumType,
        shape: &[usize],
        alignment: usize,
    ) -> TractResult<Tensor> {
        let bytes = shape.iter().cloned().product::<usize>() * dt.size_of();
        let storage = StorageKind::Dense(DenseStorage::from(unsafe {
            Blob::new_for_size_and_align(bytes, alignment)
        }));
        let mut tensor = Tensor { strides: tvec!(), dt, shape: shape.into(), storage, len: 0 };
        if tensor.shape.len() == 0 {
            tensor.len = 1;
        } else {
            tensor.update_strides_and_len();
        }
        if !tensor.storage.is_empty() {
            if dt == String::datum_type() || dt == Blob::datum_type() {
                // assumes zero-initialized string and blob are valid
                tensor.dense_storage_mut().as_bytes_mut().fill(0);
            } else if dt == TDim::datum_type() {
                unsafe {
                    tensor
                        .as_slice_mut_unchecked::<TDim>()
                        .iter_mut()
                        .for_each(|dim| std::ptr::write(dim, TDim::zero()))
                }
            } else if dt == Opaque::datum_type() {
                unsafe {
                    tensor.as_slice_mut_unchecked::<Opaque>().iter_mut().for_each(|p| {
                        std::ptr::write(p, Opaque::default());
                    })
                };
            } else if cfg!(debug_assertions) {
                assert!(dt.is_copy());
                if dt == DatumType::F32 {
                    tensor.fill_t(f32::NAN).unwrap();
                } else {
                    // safe, non copy types have been dealt with
                    tensor.as_bytes_mut().iter_mut().for_each(|x| *x = (-1i8) as u8);
                }
            }
        }
        Ok(tensor)
    }

    pub fn stack_tensors(
        axis: usize,
        tensors: &[impl std::borrow::Borrow<Tensor>],
    ) -> TractResult<Tensor> {
        ensure!(tensors.len() > 0);
        let rank = tensors[0].borrow().rank();
        ensure!(axis < rank);
        ensure!(tensors.iter().all(|t| t.borrow().rank() == rank));
        let dt = tensors[0].borrow().datum_type();
        ensure!(tensors.iter().all(|t| t.borrow().datum_type() == dt));
        let mut shape: TVec<usize> = tensors[0].borrow().shape().into();
        for ax in 0..rank {
            if ax != axis {
                ensure!(tensors.iter().all(|t| t.borrow().shape()[ax] == shape[ax]));
            }
        }
        shape[axis] = tensors.iter().map(|v| v.borrow().shape()[axis]).sum();
        unsafe {
            let mut result = Tensor::uninitialized_dt(dt, &shape)?;
            if dt.is_copy() && shape[..axis].iter().all(|d| *d == 1) {
                let mut offset = 0isize;
                for v in tensors {
                    let v = v.borrow();
                    let len = v.storage.byte_len();
                    std::ptr::copy_nonoverlapping(
                        v.dense_storage().as_ptr(),
                        result.dense_storage_mut().as_mut_ptr().offset(offset),
                        len,
                    );
                    offset += len as isize;
                }
            } else {
                let mut offset = 0;
                for t in tensors {
                    let t = t.borrow();
                    let len = t.shape()[axis];
                    result.assign_slice_from_resolved(offset..offset + len, t, 0..len, axis);
                    offset += len;
                }
            }

            Ok(result)
        }
    }

    pub fn clear<T: Datum + num_traits::Zero + Clone>(&mut self) -> TractResult<()> {
        self.fill_t(T::zero())
    }

    pub fn zero<T: Datum + num_traits::Zero>(shape: &[usize]) -> TractResult<Tensor> {
        unsafe {
            let mut t = Tensor::uninitialized::<T>(shape)?;
            t.clear::<T>()?;
            Ok(t)
        }
    }

    pub fn zero_scalar<T: Datum + num_traits::Zero>() -> TractResult<Tensor> {
        Tensor::zero::<T>(&[])
    }

    pub fn zero_scalar_dt(dt: DatumType) -> TractResult<Tensor> {
        Tensor::zero_dt(dt, &[])
    }

    pub fn zero_dt(dt: DatumType, shape: &[usize]) -> TractResult<Tensor> {
        Tensor::zero_aligned_dt(dt, shape, vector_size())
    }

    pub fn fill_t<T: Datum + Clone>(&mut self, value: T) -> TractResult<()> {
        self.try_as_dense_mut()?
            .as_slice_mut::<T>()?
            .iter_mut()
            .for_each(|item| *item = value.clone());
        Ok(())
    }

    pub fn zero_aligned_dt(
        dt: DatumType,
        shape: &[usize],
        alignment: usize,
    ) -> TractResult<Tensor> {
        if shape.iter().product::<usize>() == 0 {
            unsafe { return Tensor::uninitialized_dt(dt, shape) };
        }
        if dt.is_quantized() {
            unsafe {
                let mut t = Tensor::uninitialized_dt(dt, shape)?;
                let zp = dt.zp_scale().0;
                match dt.unquantized() {
                    DatumType::I8 => t
                        .try_as_dense_mut()?
                        .as_slice_mut::<i8>()?
                        .iter_mut()
                        .for_each(|item| *item = zp as _),
                    DatumType::U8 => t
                        .try_as_dense_mut()?
                        .as_slice_mut::<u8>()?
                        .iter_mut()
                        .for_each(|item| *item = zp as _),
                    DatumType::I32 => t
                        .try_as_dense_mut()?
                        .as_slice_mut::<i32>()?
                        .iter_mut()
                        .for_each(|item| *item = zp as _),
                    _ => unreachable!(),
                }
                Ok(t)
            }
        } else {
            dispatch_zerolike!(Self::zero_aligned(dt)(shape, alignment))
        }
    }

    pub fn zero_aligned<T: Datum + num_traits::Zero>(
        shape: &[usize],
        alignment: usize,
    ) -> TractResult<Tensor> {
        unsafe {
            let mut tensor = Self::uninitialized_aligned::<T>(shape, alignment)?;
            tensor.clear::<T>()?;
            Ok(tensor)
        }
    }

    /// Create a tensor with a given shape and a slice of elements.
    /// The data is copied and aligned to size of T.
    pub fn from_shape<T: Datum + Copy>(shape: &[usize], data: &[T]) -> TractResult<Tensor> {
        Self::from_shape_align(shape, data, vector_size())
    }

    /// Create a tensor with a given shape and a slice of elements.
    /// The data is copied and aligned to given alignment.
    pub fn from_shape_align<T: Datum + Copy>(
        shape: &[usize],
        data: &[T],
        align: usize,
    ) -> TractResult<Tensor> {
        ensure!(
            data.len() == shape.iter().product::<usize>(),
            "Shape product must be equal to data length"
        );
        unsafe {
            let bytes = std::slice::from_raw_parts(
                data.as_ptr() as *const u8,
                data.len() * T::datum_type().size_of(),
            );
            let dt = T::datum_type();
            Self::from_raw_dt_align(dt, shape, bytes, align)
        }
    }

    /// Create a tensor from raw data.
    ///
    /// It copies the data, aligning it to the size of T.
    pub unsafe fn from_raw<T: Datum>(shape: &[usize], content: &[u8]) -> TractResult<Tensor> {
        unsafe { Tensor::from_raw_dt(T::datum_type(), shape, content) }
    }

    pub unsafe fn from_raw_aligned<T: Datum>(
        shape: &[usize],
        content: &[u8],
        align: usize,
    ) -> TractResult<Tensor> {
        unsafe { Tensor::from_raw_dt_align(T::datum_type(), shape, content, align) }
    }

    pub unsafe fn from_raw_dt(
        dt: DatumType,
        shape: &[usize],
        content: &[u8],
    ) -> TractResult<Tensor> {
        unsafe { Self::from_raw_dt_align(dt, shape, content, vector_size()) }
    }

    pub unsafe fn from_raw_dt_align(
        dt: DatumType,
        shape: &[usize],
        content: &[u8],
        align: usize,
    ) -> TractResult<Tensor> {
        let mut tensor = unsafe { Tensor::uninitialized_aligned_dt(dt, shape, align) }?;
        tensor.as_bytes_mut().copy_from_slice(content);
        Ok(tensor)
    }

    pub unsafe fn from_slice_align<T: Datum>(content: &[T], align: usize) -> TractResult<Tensor> {
        let bytes = if content.len() == 0 {
            &[]
        } else {
            unsafe {
                std::slice::from_raw_parts(
                    content.as_ptr() as *const u8,
                    content.len() * T::datum_type().size_of(),
                )
            }
        };
        unsafe { Self::from_raw_dt_align(T::datum_type(), &[content.len()], bytes, align) }
    }

    /// Get the number of dimensions (or axes) of the tensor.
    #[inline]
    pub fn rank(&self) -> usize {
        self.shape.len()
    }

    /// Get the shape of the tensor.
    #[inline]
    pub fn shape(&self) -> &[usize] {
        &self.shape
    }

    /// Get the number of values in the tensor.
    #[inline]
    #[allow(clippy::len_without_is_empty)]
    pub fn len(&self) -> usize {
        self.len
    }

    /// Get the number of valeus in the tensor.
    #[inline]
    #[allow(clippy::len_without_is_empty)]
    pub fn volume(&self) -> usize {
        self.len
    }

    /// Get the shape of the tensor.
    #[inline]
    pub fn strides(&self) -> &[isize] {
        &self.strides
    }

    fn update_strides_and_len(&mut self) {
        self.strides.clear();
        if self.shape.len() == 0 {
            self.len = 1;
            return;
        }
        compute_natural_stride_to(&mut self.strides, &self.shape);
        self.len = unsafe { *self.strides.get_unchecked(0) as usize * self.shape.get_unchecked(0) };
    }

    /// Force the tensor shape, no consistency check.
    pub unsafe fn set_shape_unchecked(&mut self, shape: &[usize]) {
        if shape != &*self.shape {
            self.shape.clear();
            self.shape.extend_from_slice(shape);
            self.update_strides_and_len();
        }
    }

    /// Force the tensor shape and strides, no consistency check.
    pub unsafe fn set_geometry_unchecked(&mut self, shape: &[usize], strides: &[isize]) {
        self.shape.clear();
        self.shape.extend_from_slice(shape);
        self.strides.clear();
        self.strides.extend_from_slice(strides);
    }

    /// Force the tensor shape.
    pub fn set_shape(&mut self, shape: &[usize]) -> TractResult<()> {
        if self.len() != shape.iter().product::<usize>() {
            bail!("Invalid reshape {:?} to {:?}", self.shape, shape);
        }
        unsafe { self.set_shape_unchecked(shape) }
        Ok(())
    }

    pub fn permute_axes(self, axes: &[usize]) -> TractResult<Tensor> {
        ensure!(axes.iter().duplicates().next().is_none());
        ensure!(axes.iter().all(|a| *a < self.rank()));
        unsafe {
            #[inline]
            unsafe fn permute<T: Datum>(axes: &[usize], input: Tensor) -> Tensor {
                unsafe { input.into_array_unchecked::<T>().permuted_axes(axes).into_tensor() }
            }
            let dt = self.datum_type();
            let mut t = dispatch_datum_by_size!(permute(self.datum_type())(axes, self));
            t.set_datum_type(dt);
            Ok(t)
        }
    }

    pub fn move_axis(self, from: usize, to: usize) -> TractResult<Tensor> {
        let mut permutation: Vec<usize> = (0..self.rank()).collect();
        permutation.remove(from);
        permutation.insert(to, from);
        self.permute_axes(&permutation)
    }

    pub fn collapse_axis_with_next(mut self, axis: usize) -> Tensor {
        let removed = self.shape.remove(axis + 1);
        self.shape[axis] *= removed;
        self.update_strides_and_len();
        self
    }

    pub fn split_axis(mut self, axis: usize, outer_dim: usize) -> TractResult<Tensor> {
        if self.shape[axis] % outer_dim != 0 {
            bail!(
                "Invalid axis split, shape is {:?}, axis split at {}, outer {}",
                self.shape,
                axis,
                outer_dim
            );
        }
        self.shape.insert(axis + 1, self.shape[axis] / outer_dim);
        self.shape[axis] = outer_dim;
        self.update_strides_and_len();
        Ok(self)
    }

    /// Reshape the tensor to `shape`.
    pub fn into_shape(mut self, shape: &[usize]) -> TractResult<Tensor> {
        self.set_shape(shape)?;
        Ok(self)
    }

    pub fn insert_axis(&mut self, axis: usize) -> TractResult<()> {
        self.shape.insert(axis, 1);
        self.strides.insert(axis, self.strides.get(axis).copied().unwrap_or(1));
        Ok(())
    }

    pub fn remove_axis(&mut self, axis: usize) -> TractResult<()> {
        ensure!(self.shape[axis] == 1, "Remove a non-1 axis: axis {} in {:?}", axis, self);
        self.shape.remove(axis);
        self.strides.remove(axis);
        Ok(())
    }

    pub fn broadcast_into_rank(mut self, rank: usize) -> TractResult<Tensor> {
        self.broadcast_to_rank(rank)?;
        self.update_strides_and_len();
        Ok(self)
    }

    pub fn broadcast_to_rank(&mut self, rank: usize) -> TractResult<()> {
        if rank < self.rank() {
            bail!("Can only broadcast to higher rank")
        }
        while self.shape.len() < rank {
            self.shape.insert(0, 1)
        }
        self.update_strides_and_len();
        Ok(())
    }

    pub fn broadcast_scalar_to_shape(&self, shape: &[usize]) -> TractResult<Tensor> {
        if self.rank() > 0 {
            bail!("broadcast_scalar_to_shape called on {:?}, which is not a salar", self);
        }
        unsafe fn make<T: Datum>(src: &Tensor, dst: &mut Tensor) {
            unsafe {
                let value: &T = src.to_scalar_unchecked::<T>();
                dst.as_slice_mut_unchecked::<T>().iter_mut().for_each(|item| *item = value.clone())
            };
        }
        unsafe {
            let mut t = Tensor::uninitialized_dt(self.datum_type(), shape)?;
            dispatch_datum_by_size!(make(self.datum_type())(self, &mut t));
            Ok(t)
        }
    }

    fn broadcast_to_shape_t<T: Datum>(&self, shape: &[usize]) -> TractResult<Tensor> {
        unsafe {
            let view = self.to_array_view_unchecked::<T>();
            let mut output = view
                .broadcast(shape)
                .with_context(|| format!("Broadcasting {view:?} to {shape:?}"))?
                .into_owned()
                .into_tensor();
            output.set_datum_type(self.datum_type());
            Ok(output)
        }
    }

    pub fn broadcast_to_shape(&self, shape: &[usize]) -> TractResult<Tensor> {
        dispatch_datum!(Self::broadcast_to_shape_t(self.dt)(self, shape))
    }

    pub fn broadcast_vector_to_shape(&self, shape: &[usize], axis: usize) -> TractResult<Tensor> {
        ensure!(self.rank() == 1);
        ensure!(shape[axis] == self.len());
        if !self.datum_type().is_copy() {
            let mut vec_shape = vec![1; shape.len()];
            vec_shape[axis] = self.len();
            return self.clone().into_shape(&vec_shape)?.broadcast_to_shape(shape);
        }
        unsafe {
            let mut output = Tensor::uninitialized_dt(self.datum_type(), shape)?;
            if output.len() == 0 {
                return Ok(output);
            }
            let inner_len = shape[axis + 1..].iter().product::<usize>();

            unsafe fn splat<T>(input: &Tensor, output: &mut Tensor, inner_len: usize)
            where
                T: Datum + Copy,
            {
                unsafe {
                    for ix in 0..input.len() {
                        let value: T = input.as_slice_unchecked()[ix];
                        output.as_slice_mut_unchecked::<T>()[ix * inner_len..(ix + 1) * inner_len]
                            .iter_mut()
                            .for_each(|item| *item = value);
                    }
                }
            }
            dispatch_copy_by_size!(splat(self.datum_type())(&self, &mut output, inner_len));

            let outer_len = shape[0..axis].iter().product::<usize>();
            let repeat_bytes_len = inner_len * self.as_bytes().len();
            let bytes = output.as_bytes_mut();
            for ix in 1..outer_len {
                bytes.copy_within(0..repeat_bytes_len, ix * repeat_bytes_len);
            }

            Ok(output)
        }
    }

    pub fn assign_slice(
        &mut self,
        range: impl std::ops::RangeBounds<usize>,
        src: &Tensor,
        src_range: impl std::ops::RangeBounds<usize>,
        axis: usize,
    ) -> TractResult<()> {
        ensure!(self.rank() == src.rank());
        ensure!(axis < self.rank());
        let range = clip_range_bounds(self.shape[axis], range);
        let src_range = clip_range_bounds(src.shape[axis], src_range);
        ensure!(
            src.datum_type() == self.datum_type(),
            "Attempt to assign into {:?} from {:?}, datum type mismatch",
            self.datum_type(),
            src.datum_type()
        );
        ensure!(
            src_range.len() == range.len(),
            "Attempt to assign a range of {:?} from a range of {:?}",
            range,
            src_range,
        );
        ensure!(
            itertools::izip!(0.., self.shape(), src.shape())
                .all(|(ix, dst, src)| ix == axis || src == dst),
            "Attempt to assign a {}-axis range of {:?} from a range of {:?}",
            axis,
            self,
            src
        );
        ensure!(
            src_range.end <= src.shape()[axis],
            "Assigning from invalid slice (axis {}, {:?}) of {:?}",
            axis,
            src_range,
            src
        );
        ensure!(
            range.end <= self.shape()[axis],
            "Assigning to invalid slice (axis {}, {:?}) of {:?}",
            axis,
            range,
            self
        );
        unsafe { self.assign_slice_from_resolved(range, src, src_range, axis) };
        Ok(())
    }

    pub unsafe fn assign_slice_unchecked(
        &mut self,
        range: impl std::ops::RangeBounds<usize>,
        src: &Tensor,
        src_range: impl std::ops::RangeBounds<usize>,
        axis: usize,
    ) {
        let range = clip_range_bounds(self.shape[axis], range);
        let src_range = clip_range_bounds(src.shape[axis], src_range);
        unsafe { self.assign_slice_from_resolved(range, src, src_range, axis) };
    }

    #[allow(clippy::ptr_eq)]
    unsafe fn assign_slice_from_resolved(
        &mut self,
        range: std::ops::Range<usize>,
        src: &Tensor,
        src_range: std::ops::Range<usize>,
        axis: usize,
    ) {
        unsafe {
            use ndarray::Slice;
            unsafe fn assign_slice_t<T: Datum>(
                to: &mut Tensor,
                to_range: Range<usize>,
                from: &Tensor,
                from_range: Range<usize>,
                axis: usize,
            ) {
                unsafe {
                    to.to_array_view_mut_unchecked::<T>()
                        .slice_axis_mut(Axis(axis), Slice::from(to_range))
                        .assign(
                            &from
                                .to_array_view_unchecked::<T>()
                                .slice_axis(Axis(axis), Slice::from(from_range)),
                        )
                }
            }
            if self.datum_type().is_copy() && self.shape[..axis].iter().all(|d| *d == 1) {
                let stride = self.strides[axis] as usize * self.datum_type().size_of();
                let dst_start = (stride * range.start) as isize;
                let src_start = (stride * src_range.start) as isize;
                let len = stride * range.len();
                if len > 0 {
                    if self.dense_storage().as_ptr() != src.dense_storage().as_ptr() {
                        std::ptr::copy_nonoverlapping(
                            src.dense_storage().as_ptr().offset(src_start),
                            self.dense_storage_mut().as_mut_ptr().offset(dst_start),
                            len,
                        );
                    } else {
                        std::ptr::copy(
                            src.dense_storage().as_ptr().offset(src_start),
                            self.dense_storage_mut().as_mut_ptr().offset(dst_start),
                            len,
                        );
                    }
                }
            } else {
                dispatch_datum!(assign_slice_t(self.datum_type())(
                    self, range, src, src_range, axis
                ));
            }
        }
    }

    /// Get the datum type of the tensor.
    #[inline]
    pub fn datum_type(&self) -> DatumType {
        self.dt
    }

    /// Set the datum type of the tensor.
    #[inline]
    pub unsafe fn set_datum_type(&mut self, dt: DatumType) {
        self.dt = dt
    }

    /// Dump the tensor in a human readable form.
    ///
    /// `force_full` will force the tensor to be dump in full even if it is big.
    pub fn dump(&self, force_full: bool) -> TractResult<String> {
        unsafe fn dump_t<D: Datum>(tensor: &Tensor, n: usize) -> String {
            unsafe {
                if let Some(qp) = tensor.datum_type().qparams() {
                    let integers = tensor.cast_to::<i32>().unwrap();
                    integers.as_slice_unchecked::<i32>()[0..n]
                        .iter()
                        .map(|x| format!("[{}]({})", x, qp.dq(*x)))
                        .join(", ")
                } else {
                    tensor.as_slice_unchecked::<D>()[0..n].iter().join(", ")
                }
            }
        }
        unsafe {
            let trunc = self.len() > 12 && !force_full;
            let data = dispatch_datum!(dump_t(self.datum_type())(
                self,
                if trunc { 12 } else { self.len() }
            ));
            Ok(format!(
                "{},{:?} {}{}",
                self.shape.iter().join(","),
                self.dt,
                data,
                if trunc { "..." } else { "" }
            ))
        }
    }

    /// Compare two tensors, allowing for rounding errors.
    pub fn close_enough(
        &self,
        other: &Self,
        approx: impl Into<Approximation> + std::fmt::Debug,
    ) -> TractResult<()> {
        let approx = approx.into();
        if self.shape() != other.shape() {
            bail!("Shape mismatch {:?} != {:?}", self.shape(), other.shape())
        }
        let (atol, rtol, outliers) = approx.atol_rtol_outliers(&self.datum_type());
        let ma = self.cast_to::<f32>()?;
        let ma = ma.to_dense_array_view::<f32>()?;
        let mb = other.cast_to::<f32>()?;
        let mb = mb.to_dense_array_view::<f32>()?;
        let mut first_outlier = None;
        let mut outliers_count = 0;
        ndarray::indices_of(&ma).into_iter().for_each(|indices| {
            let a = ma[&indices];
            let b = mb[&indices];
            if !((a.is_nan() && b.is_nan())
                || (a.is_infinite() && b.is_infinite() && a.signum() == b.signum())
                || (a - b).abs() <= atol as f32 + rtol as f32 * b.abs())
            {
                if outliers_count == 0 {
                    first_outlier = Some(indices.as_array_view().to_vec());
                }
                outliers_count += 1;
            }
        });
        if self.volume() > 0 && outliers_count as f64 / self.volume() as f64 > outliers {
            let indices = first_outlier.unwrap();
            let a = ma[&*indices];
            let b = mb[&*indices];
            bail!(
                "Mismatch. First outlier: {:?} for {:?}) at {:?} {} != {}. Outliers: {} / {} = {:0.5} > {:0.5}.",
                approx,
                self.datum_type(),
                indices,
                a,
                b,
                outliers_count,
                self.volume(),
                outliers_count as f64 / self.volume() as f64,
                outliers
            );
        }
        Ok(())
    }

    /// Transform the tensor into a `ndarray::Array`.
    pub fn into_dense_array<D: Datum>(self) -> TractResult<ArrayD<D>> {
        Ok(self.to_dense_array_view::<D>()?.to_owned())
    }

    /// Transform the tensor into a `ndarray::Array`.
    pub unsafe fn into_array_unchecked<D: Datum>(self) -> ArrayD<D> {
        unsafe { self.to_array_view_unchecked::<D>().to_owned() }
    }

    /// Returns a dense array view of the tensor.
    ///
    /// Errors if the storage is not dense or the datum type does not match `D`.
    #[inline]
    pub fn to_dense_array_view<D: Datum>(&self) -> TractResult<ArrayViewD<'_, D>> {
        self.try_as_dense()?.to_array_view::<D>()
    }

    /// Returns a mutable dense array view of the tensor.
    ///
    /// Errors if the storage is not dense or the datum type does not match `D`.
    #[inline]
    pub fn to_dense_array_view_mut<D: Datum>(&mut self) -> TractResult<ArrayViewMutD<'_, D>> {
        self.check_for_access::<D>()?;
        ensure!(self.storage.as_dense_mut().is_some(), "Tensor storage is not dense");
        unsafe { Ok(self.to_array_view_mut_unchecked()) }
    }

    fn check_for_access<D: Datum>(&self) -> TractResult<()> {
        ensure!(
            self.datum_type().unquantized() == D::datum_type().unquantized(),
            "Tensor datum type error: tensor is {:?}, accessed as {:?}",
            self.datum_type(),
            D::datum_type(),
        );
        Ok(())
    }

    /// Transform the data as a `ndarray::Array`.
    pub unsafe fn to_array_view_unchecked<D: Datum>(&self) -> ArrayViewD<'_, D> {
        if self.len() != 0 {
            unsafe {
                ArrayViewD::from_shape_ptr(&*self.shape, self.dense_storage().as_ptr() as *const D)
            }
        } else {
            ArrayViewD::from_shape(&*self.shape, &[]).unwrap()
        }
    }

    /// Transform the data as a mutable `ndarray::Array`.
    pub unsafe fn to_array_view_mut_unchecked<D: Datum>(&mut self) -> ArrayViewMutD<'_, D> {
        if self.len() != 0 {
            unsafe {
                let ptr = self.dense_storage_mut().as_mut_ptr() as *mut D;
                ArrayViewMutD::from_shape_ptr(&*self.shape, ptr)
            }
        } else {
            ArrayViewMutD::from_shape(&*self.shape, &mut []).unwrap()
        }
    }

    /// Access the data as a pointer.
    pub fn as_ptr<D: Datum>(&self) -> TractResult<*const D> {
        self.check_for_access::<D>()?;
        Ok(self.dense_storage().as_ptr() as *const D)
    }

    /// Access the data as a pointer.
    pub unsafe fn as_ptr_unchecked<D: Datum>(&self) -> *const D {
        self.dense_storage().as_ptr() as *const D
    }

    /// Access the data as a pointer.
    pub unsafe fn as_ptr_mut_unchecked<D: Datum>(&mut self) -> *mut D {
        self.dense_storage_mut().as_mut_ptr() as *mut D
    }

    /// Access the data as a mutable pointer.
    pub fn as_ptr_mut<D: Datum>(&mut self) -> TractResult<*mut D> {
        self.as_ptr::<D>().map(|p| p as *mut D)
    }

    /// Access the data as a slice.
    pub unsafe fn as_slice_unchecked<D: Datum>(&self) -> &[D] {
        if self.storage.byte_len() == 0 {
            &[]
        } else {
            unsafe { std::slice::from_raw_parts::<D>(self.as_ptr_unchecked(), self.len()) }
        }
    }

    /// Access the data as a mutable slice.
    pub unsafe fn as_slice_mut_unchecked<D: Datum>(&mut self) -> &mut [D] {
        if self.storage.byte_len() == 0 {
            &mut []
        } else {
            unsafe { std::slice::from_raw_parts_mut::<D>(self.as_ptr_mut_unchecked(), self.len()) }
        }
    }

    /// Make the tensor a scalar tensor (assumes it contains a single value).
    pub fn to_scalar_tensor(&self) -> TractResult<Tensor> {
        fn to_scalar_tensor_t<D: Datum>(t: &Tensor) -> TractResult<Tensor> {
            Ok(litteral::tensor0(t.try_as_dense()?.to_scalar::<D>()?.clone()))
        }
        dispatch_datum!(to_scalar_tensor_t(self.datum_type())(self))
    }

    /// Access the data as a scalar.
    pub unsafe fn to_scalar_unchecked<D: Datum>(&self) -> &D {
        unsafe { &*(self.dense_storage().as_ptr() as *const D) }
    }

    /// Mutable access the data as a scalar.
    pub fn to_scalar_mut<D: Datum>(&mut self) -> TractResult<&mut D> {
        self.check_for_access::<D>()?;
        if self.len() == 0 {
            bail!("to_scalar_mut called on empty tensor ({:?})", self)
        }
        if self.len() > 1 {
            bail!("to_scalar called on a tensor with multiple values ({:?})", self)
        }
        unsafe { Ok(self.to_scalar_mut_unchecked()) }
    }

    /// Mutable access the data as a scalar.
    pub unsafe fn to_scalar_mut_unchecked<D: Datum>(&mut self) -> &mut D {
        unsafe { &mut *(self.dense_storage_mut().as_mut_ptr() as *mut D) }
    }

    pub fn as_bytes(&self) -> &[u8] {
        self.dense_storage().as_bytes()
    }

    pub fn as_bytes_mut(&mut self) -> &mut [u8] {
        self.dense_storage_mut().as_bytes_mut()
    }

    unsafe fn is_uniform_t<T: Datum>(&self) -> bool {
        let slice = unsafe { self.as_slice_unchecked::<T>() };
        slice[1..].iter().all(|x| x == &slice[0])
    }

    pub fn is_uniform(&self) -> bool {
        if self.len() <= 1 {
            return true;
        }
        unsafe { dispatch_datum!(Tensor::is_uniform_t(self.datum_type())(self)) }
    }

    unsafe fn as_uniform_t<T: Datum>(&self) -> Tensor {
        let v: T = unsafe { self.as_slice_unchecked::<T>() }[0].clone();
        litteral::tensor0(v)
    }

    pub fn as_uniform(&self) -> Option<Tensor> {
        if self.len() >= 1 && self.is_uniform() {
            unsafe {
                let mut t = dispatch_datum!(Tensor::as_uniform_t(self.datum_type())(self));
                t.set_datum_type(self.datum_type());
                Some(t)
            }
        } else {
            None
        }
    }

    pub fn is_all_zero(&self) -> TractResult<bool> {
        Ok(self.len() == 0 || self.as_uniform().map(|t| t.is_zero().unwrap()).unwrap_or(false))
    }

    pub fn is_zero(&self) -> TractResult<bool> {
        Ok(self == &Tensor::zero_scalar_dt(self.dt)?)
    }

    unsafe fn natural_cast<
        Source: Datum + num_traits::AsPrimitive<Target>,
        Target: Datum + Copy,
    >(
        &self,
        other: &mut Tensor,
    ) {
        unsafe {
            self.as_slice_unchecked::<Source>()
                .iter()
                .zip(other.as_slice_mut_unchecked::<Target>().iter_mut())
                .for_each(|(s, d)| *d = s.as_())
        };
    }

    unsafe fn cast_number_to_bool<Source: Datum + num_traits::Zero>(&self, other: &mut Tensor) {
        unsafe {
            self.as_slice_unchecked::<Source>()
                .iter()
                .zip(other.as_slice_mut_unchecked::<bool>().iter_mut())
                .for_each(|(s, d)| *d = !s.is_zero());
        }
    }

    unsafe fn cast_from_string<Target: Datum + core::str::FromStr>(
        &self,
        other: &mut Tensor,
    ) -> TractResult<()> {
        unsafe {
            for (s, d) in self
                .as_slice_unchecked::<String>()
                .iter()
                .zip(other.as_slice_mut_unchecked::<Target>().iter_mut())
            {
                *d = s
                    .parse()
                    .map_err(|_| format_err!("Can not parse as {:?}", Target::datum_type()))?;
            }
            Ok(())
        }
    }

    unsafe fn cast_to_string<Source: Datum>(&self, other: &mut Tensor) {
        unsafe {
            for (s, d) in self
                .as_slice_unchecked::<Source>()
                .iter()
                .zip(other.as_slice_mut_unchecked::<String>().iter_mut())
            {
                *d = s.to_string()
            }
        }
    }

    /// Optionnaly convert data to a tensor for a new DatumType.
    pub fn cast_to<D: Datum>(&self) -> TractResult<Cow<'_, Tensor>> {
        self.cast_to_dt(D::datum_type())
    }

    /// Optionnaly convert data to a tensor for a new DatumType.
    #[allow(clippy::redundant_closure_call)]
    pub fn cast_to_dt(&self, dst_dt: DatumType) -> TractResult<Cow<'_, Tensor>> {
        unsafe {
            if self.dt == dst_dt {
                return Ok(Cow::Borrowed(self));
            }
            if self.dt == TDim::datum_type() && (dst_dt.is_integer() || dst_dt.is_float()) {
                let slice = self.as_slice_unchecked::<TDim>();
                let mut ints = Self::uninitialized::<i64>(&self.shape)?;
                let ints_slice = ints.as_slice_mut_unchecked::<i64>();
                for i in 0..self.len() {
                    ints_slice[i] = slice[i].to_i64()?;
                }
                return Ok(Cow::Owned(ints.cast_to_dt(dst_dt)?.into_owned()));
            }
            if self.dt == bool::datum_type()
                && (dst_dt.is_integer() || dst_dt.is_float() || dst_dt == TDim::datum_type())
            {
                let slice = self.as_slice_unchecked::<bool>();
                let mut ints = Self::uninitialized::<i8>(&self.shape)?;
                let ints_slice = ints.as_slice_mut_unchecked::<i8>();
                for i in 0..self.len() {
                    ints_slice[i] = slice[i] as usize as i8;
                }
                return Ok(Cow::Owned(ints.cast_to_dt(dst_dt)?.into_owned()));
            }
            let mut result = Self::uninitialized_dt(dst_dt, &self.shape)?;
            if self.dt == DatumType::String {
                dispatch_numbers!(Self::cast_from_string(dst_dt)(self, &mut result))?;
                return Ok(Cow::Owned(result));
            }
            if dst_dt == DatumType::String {
                dispatch_datum!(Self::cast_to_string(self.dt)(self, &mut result));
                return Ok(Cow::Owned(result));
            }
            macro_rules! n {
                ($source:ty) => {
                    if <$source>::datum_type() == self.datum_type() {
                        match dst_dt {
                            DatumType::I8 => self.natural_cast::<$source, i8>(&mut result),
                            DatumType::I16 => self.natural_cast::<$source, i16>(&mut result),
                            DatumType::I32 => self.natural_cast::<$source, i32>(&mut result),
                            DatumType::I64 => self.natural_cast::<$source, i64>(&mut result),
                            DatumType::U8 => self.natural_cast::<$source, u8>(&mut result),
                            DatumType::U16 => self.natural_cast::<$source, u16>(&mut result),
                            DatumType::U32 => self.natural_cast::<$source, u32>(&mut result),
                            DatumType::U64 => self.natural_cast::<$source, u64>(&mut result),
                            DatumType::F16 => self.natural_cast::<$source, f16>(&mut result),
                            DatumType::F32 => self.natural_cast::<$source, f32>(&mut result),
                            DatumType::F64 => self.natural_cast::<$source, f64>(&mut result),
                            DatumType::TDim => {
                                let ints = self.cast_to::<i32>()?;
                                let slice = ints.as_slice_unchecked::<i32>();
                                let result = result.as_slice_mut_unchecked::<TDim>();
                                for i in 0..self.len() {
                                    result[i] = slice[i].into();
                                }
                            }
                            DatumType::Bool => self.cast_number_to_bool::<$source>(&mut result),
                            _ => todo!(),
                        }
                        return Ok(Cow::Owned(result));
                    };
                };
            }
            //If there is no quantization
            if !dst_dt.is_quantized() && !self.datum_type().is_quantized() {
                n!(u8);
                n!(u16);
                n!(u32);
                n!(u64);
                n!(i8);
                n!(i16);
                n!(i32);
                n!(i64);
                n!(f16);
                n!(f32);
                n!(f64);
            } else {
                let (s_zp, s_scale) = self.datum_type().zp_scale();
                let (d_zp, d_scale) = dst_dt.zp_scale();
                if self.datum_type().is_quantized() && dst_dt.is_float() {
                    macro_rules! q_to_fp {
                        ($source:ty, $dest:ty) => {
                            if <$source>::datum_type().unquantized()
                                == self.datum_type().unquantized()
                                && <$dest>::datum_type().unquantized() == dst_dt.unquantized()
                            {
                                self.as_slice_unchecked::<$source>()
                                    .iter()
                                    .zip(result.as_slice_mut_unchecked::<$dest>().iter_mut())
                                    .for_each(|(&s, d)| {
                                        *d = (s as $dest - s_zp as $dest) * s_scale as $dest;
                                    });
                                return Ok(Cow::Owned(result));
                            }
                        };
                    }
                    q_to_fp!(i8, f64);
                    q_to_fp!(i8, f32);
                    q_to_fp!(u8, f64);
                    q_to_fp!(u8, f32);
                }
                //TODO: optimize scale_by
                macro_rules! q8_to_q8 {
                    ($typ:ty) => {
                        if dst_dt.unquantized() == <$typ>::datum_type() {
                            self.as_slice_unchecked::<$typ>()
                                .iter()
                                .zip(result.as_slice_mut_unchecked::<$typ>().iter_mut())
                                .for_each(|(&s, d)| {
                                    *d = (d_zp as i32
                                        + scale_by(s as i32 - s_zp as i32, s_scale / d_scale))
                                    .clamp_cast()
                                });
                            return Ok(Cow::Owned(result));
                        }
                    };
                }

                macro_rules! q_via_f32 {
                    ($source:ty, $dest:ty, $round:expr) => {
                        if <$source>::datum_type().unquantized() == self.datum_type().unquantized()
                            && <$dest>::datum_type().unquantized() == dst_dt.unquantized()
                        {
                            self.as_slice_unchecked::<$source>()
                                .iter()
                                .zip(result.as_slice_mut_unchecked::<$dest>().iter_mut())
                                .for_each(|(&s, d)| {
                                    let s_float = (s as f32 - s_zp as f32) * s_scale as f32;
                                    let d_float = s_float as f32 / d_scale as f32 + d_zp as f32;
                                    *d = $round(d_float);
                                });
                            return Ok(Cow::Owned(result));
                        }
                    };
                }

                macro_rules! q_n {
                    (clamp $source:ty, $dest:ty) => {{
                        if <$source>::datum_type().unquantized() == self.datum_type().unquantized()
                            && <$dest>::datum_type().unquantized() == dst_dt.unquantized()
                        {
                            self.as_slice_unchecked::<$source>()
                                .iter()
                                .zip(result.as_slice_mut_unchecked::<$dest>().iter_mut())
                                .for_each(|(&s, d)| {
                                    *d = s.clamp_cast();
                                });
                            return Ok(Cow::Owned(result));
                        }
                    }};
                    ($source:ty, $dest:ty) => {{
                        if <$source>::datum_type().unquantized() == self.datum_type().unquantized()
                            && <$dest>::datum_type().unquantized() == dst_dt.unquantized()
                        {
                            self.as_slice_unchecked::<$source>()
                                .iter()
                                .zip(result.as_slice_mut_unchecked::<$dest>().iter_mut())
                                .for_each(|(&s, d)| {
                                    *d = s as $dest;
                                });
                            return Ok(Cow::Owned(result));
                        }
                    }};
                }

                if dst_dt.unquantized() == self.datum_type().unquantized()
                    && dst_dt.is_quantized()
                    && self.datum_type().is_quantized()
                {
                    q8_to_q8!(i8);
                    q8_to_q8!(u8);
                }

                q_via_f32!(f32, i8, |f| round_ties_to_even(f).clamp_cast());
                q_via_f32!(f32, u8, |f| round_ties_to_even(f).clamp_cast());
                q_via_f32!(f32, i32, |f| round_ties_to_even(f).clamp_cast());
                q_via_f32!(i8, f32, |f| f);
                q_via_f32!(u8, f32, |f| f);
                q_via_f32!(i32, f32, |f| f);

                if dst_dt.is_quantized() && self.datum_type().is_quantized() {
                    q_via_f32!(u8, i8, |f| round_ties_to_even(f).clamp_cast());
                    q_via_f32!(i8, u8, |f| round_ties_to_even(f).clamp_cast());
                    q_via_f32!(i32, u8, |f| round_ties_to_even(f).clamp_cast());
                    q_via_f32!(i32, i8, |f| round_ties_to_even(f).clamp_cast());
                    q_via_f32!(u8, i32, |f| round_ties_to_even(f).clamp_cast());
                    q_via_f32!(i8, i32, |f| round_ties_to_even(f).clamp_cast());

                    // ensure cast to different scale offset work
                    q_via_f32!(i8, i8, |f| round_ties_to_even(f).clamp_cast());
                    q_via_f32!(u8, u8, |f| round_ties_to_even(f).clamp_cast());
                }

                q_n!(i8, i32);
                q_n!(i8, u32);
                q_n!(u8, i32);
                q_n!(u8, u32);
                q_n!(clamp i32, i8);
                q_n!(clamp i32, u8);
                q_n!(clamp u32, i8);
                q_n!(clamp u32, u8);
                q_n!(i8, i8);
                q_n!(u8, u8);
                q_n!(i32, i32);
                q_n!(u32, u32);
            }

            bail!("Unsupported cast from {:?} to {:?}", self.dt, dst_dt)
        }
    }

    /// Access the data as a scalar, after a cast.
    pub fn cast_to_scalar<D: Datum + Copy>(&self) -> TractResult<D> {
        let casted = self.cast_to::<D>()?;
        casted.try_as_dense()?.to_scalar::<D>().copied()
    }

    /// Access the nth element of the tensor, returned as a 0-rank Tensor
    pub fn nth(&self, nth: usize) -> TractResult<Tensor> {
        if nth >= self.len() {
            bail!(
                "nth called with {}th element on a tensor of len {} ({:?}",
                nth,
                self.len(),
                self
            );
        }
        unsafe fn nth_t<T: Datum>(me: &Tensor, nth: usize, output: &mut Tensor) {
            unsafe {
                let value = me.as_slice_unchecked::<T>()[nth].clone();
                output.as_slice_mut_unchecked::<T>()[0] = value;
            }
        }
        unsafe {
            let mut output = Tensor::uninitialized_dt(self.datum_type(), &[])?;
            dispatch_datum_by_size!(nth_t(self.datum_type())(self, nth, &mut output));
            Ok(output)
        }
    }

    /// Strict equality test on tensors.
    fn eq_dt(&self, other: &Tensor) -> TractResult<bool> {
        unsafe fn eq_t<D: Datum>(me: &Tensor, other: &Tensor) -> TractResult<bool> {
            unsafe {
                if D::datum_type().is_float() {
                    return dispatch_floatlike!(float_eq_t(D::datum_type())(me, other));
                }
                Ok(izip!(me.as_slice_unchecked::<D>(), other.as_slice_unchecked::<D>())
                    .all(|(a, b)| a == b))
            }
        }

        unsafe fn float_eq_t<D: Datum + Float>(me: &Tensor, other: &Tensor) -> TractResult<bool> {
            unsafe {
                Ok(izip!(me.as_slice_unchecked::<D>(), other.as_slice_unchecked::<D>())
                    .all(|(a, b)| (a.is_nan() && b.is_nan()) || a == b))
            }
        }

        unsafe {
            Ok(self.datum_type() == other.datum_type()
                && self.shape() == other.shape()
                && dispatch_datum!(eq_t(self.dt)(self, other))?)
        }
    }

    fn from_datum<T: Datum>(mut it: ArrayD<T>) -> Tensor {
        unsafe {
            let mut t = Self::uninitialized::<T>(it.shape()).unwrap();
            if let Some(slice) = it.as_slice_mut() {
                if t.datum_type().is_copy() {
                    std::ptr::copy_nonoverlapping(
                        slice.as_ptr() as *const i8,
                        t.as_ptr_mut_unchecked(),
                        t.dense_storage().layout().size(),
                    );
                } else {
                    t.as_slice_mut_unchecked::<T>()
                        .iter_mut()
                        .zip(slice.iter_mut())
                        .for_each(|(t, s)| *t = std::mem::take(s));
                }
                return t;
            }
            if it.strides().iter().all(|&s| s > 0) && it.as_slice_memory_order().is_some() {
                let mut len_and_strides: TVec<(usize, usize)> = tvec!();
                for (len, stride) in itertools::izip!(it.shape(), it.strides(), t.strides())
                    .sorted_by_key(|(_, src, _)| *src)
                    .map(|(l, _, dst)| (*l as isize, *dst))
                {
                    if !len_and_strides.is_empty()
                        && len_and_strides.last().unwrap().1 * len_and_strides.last().unwrap().0
                            == stride as usize
                    {
                        len_and_strides.last_mut().unwrap().0 *= len as usize;
                    } else {
                        len_and_strides.push((len as usize, stride as usize));
                    }
                }
                len_and_strides.reverse();
                crate::scatter::scatter_contig_data(
                    it.as_ptr(),
                    t.as_ptr_mut_unchecked(),
                    &len_and_strides,
                );
                return t;
            }
            // finally use ndarray into_iter()
            t.as_slice_mut_unchecked().iter_mut().zip(it).for_each(|(t, a)| *t = a);
            t
        }
    }

    pub fn deep_clone(&self) -> Tensor {
        unsafe {
            let mut tensor = Tensor::uninitialized_dt(self.datum_type(), self.shape()).unwrap();
            if self.len() > 0 {
                if self.dt.is_copy() {
                    self.dense_storage().as_ptr().copy_to_nonoverlapping(
                        tensor.as_bytes_mut().as_mut_ptr(),
                        self.dense_storage().layout().size(),
                    )
                } else if self.dt == DatumType::String {
                    tensor
                        .as_slice_mut_unchecked::<String>()
                        .clone_from_slice(self.as_slice_unchecked());
                } else if self.dt == DatumType::Blob {
                    tensor
                        .as_slice_mut_unchecked::<Blob>()
                        .clone_from_slice(self.as_slice_unchecked());
                } else if self.dt == DatumType::Opaque {
                    tensor
                        .as_slice_mut_unchecked::<Opaque>()
                        .clone_from_slice(self.as_slice_unchecked());
                } else if self.dt == DatumType::TDim {
                    tensor
                        .as_slice_mut_unchecked::<TDim>()
                        .clone_from_slice(self.as_slice_unchecked());
                }
            }
            tensor
        }
    }

    pub fn slice(&self, axis: usize, start: usize, end: usize) -> TractResult<Tensor> {
        if axis >= self.rank() {
            bail!("Can not slice at axis {} tensor {:?}", axis, self);
        }
        if start > self.shape[axis] || end > self.shape[axis] || start >= end {
            bail!("Invalid slicing range {start}..{end} on axis {axis} for {self:?}");
        }
        fn slice_t<T: Datum>(
            t: &Tensor,
            axis: usize,
            start: usize,
            end: usize,
        ) -> TractResult<Tensor> {
            Ok(t.to_dense_array_view::<T>()?
                .slice_axis(ndarray::Axis(axis), (start..end).into())
                .into_owned()
                .into_tensor())
        }
        dispatch_datum!(slice_t(self.datum_type())(self, axis, start, end))
    }

    #[inline]
    pub fn view(&self) -> view::TensorView<'_> {
        unsafe { view::TensorView::view(self) }
    }

    #[inline]
    pub fn view_at_prefix(&self, prefix: &[usize]) -> TractResult<view::TensorView<'_>> {
        view::TensorView::at_prefix(self, prefix)
    }

    #[inline]
    pub fn view_offsetting(&self, coords: &[usize]) -> TractResult<view::TensorView<'_>> {
        view::TensorView::offsetting(self, coords)
    }

    #[inline]
    pub unsafe fn view_offsetting_unchecked(&self, coords: &[usize]) -> view::TensorView<'_> {
        unsafe { view::TensorView::offsetting_unchecked(self, coords) }
    }

    #[inline]
    pub fn view_mut(&mut self) -> view::TensorView<'_> {
        unsafe { view::TensorView::view(self) }
    }

    #[inline]
    pub fn view_at_prefix_mut(&mut self, prefix: &[usize]) -> TractResult<view::TensorView<'_>> {
        view::TensorView::at_prefix(self, prefix)
    }

    #[inline]
    pub fn view_offsetting_mut(&mut self, coords: &[usize]) -> TractResult<view::TensorView<'_>> {
        view::TensorView::offsetting(self, coords)
    }

    /// Offsets the tensor as an i8 type if it's an u8 type, otherwise passes it unchanged.
    pub fn offset_u8_as_i8(self: &Arc<Self>) -> Arc<Self> {
        let mut t = if let DatumType::U8 = self.dt.unquantized() {
            self.try_as_dense()
                .unwrap()
                .to_array_view::<u8>()
                .unwrap()
                .mapv(|v| v.wrapping_sub(128) as i8)
                .into_tensor()
        } else {
            return self.clone();
        };

        if let DatumType::QU8(qp) = self.dt {
            if let QParams::ZpScale { zero_point, scale } = qp {
                t.dt = DatumType::QI8(QParams::ZpScale { zero_point: zero_point - 128, scale });
            } else {
                t.dt = DatumType::QI8(qp);
            }
        }

        t.into_arc_tensor()
    }

    /// Offsets the tensor as an u8 type if it's an i8 type, otherwise passes it unchanged.
    pub fn offset_i8_as_u8(self: &Arc<Self>) -> Arc<Self> {
        let mut t = if let DatumType::I8 = self.dt.unquantized() {
            self.try_as_dense()
                .unwrap()
                .to_array_view::<i8>()
                .unwrap()
                .mapv(|v| (v as u8).wrapping_add(128))
                .into_tensor()
        } else {
            return self.clone();
        };

        if let DatumType::QI8(qp) = self.dt {
            if let QParams::ZpScale { zero_point, scale } = qp {
                t.dt = DatumType::QU8(QParams::ZpScale { zero_point: zero_point + 128, scale });
            } else {
                t.dt = DatumType::QU8(qp);
            }
        }
        t.into_arc_tensor()
    }

    pub fn to_aligned_default(&self) -> TractResult<Self> {
        if self.dt.is_copy() {
            unsafe {
                let mut t = Self::uninitialized_dt(self.dt, &self.shape)?;
                t.as_bytes_mut().copy_from_slice(self.as_bytes());
                Ok(t)
            }
        } else {
            let mut t = Self::zero_dt(self.dt, &self.shape)?;
            if self.dt == String::datum_type() {
                t.try_as_dense_mut()?
                    .as_slice_mut::<String>()?
                    .clone_from_slice(self.try_as_dense()?.as_slice()?);
            } else if self.dt == Blob::datum_type() {
                t.try_as_dense_mut()?
                    .as_slice_mut::<Blob>()?
                    .clone_from_slice(self.try_as_dense()?.as_slice()?);
            } else if self.dt == TDim::datum_type() {
                t.try_as_dense_mut()?
                    .as_slice_mut::<TDim>()?
                    .clone_from_slice(self.try_as_dense()?.as_slice()?);
            }
            Ok(t)
        }
    }

    pub fn natural_strides(shape: &[usize]) -> TVec<isize> {
        let mut strides = tvec!();
        compute_natural_stride_to(&mut strides, shape);
        strides
    }

    pub fn into_blob(mut self) -> TractResult<Blob> {
        ensure!(self.dt.is_copy());
        let storage =
            std::mem::replace(&mut self.storage, StorageKind::Dense(DenseStorage::default()));
        Ok(storage.into_dense().context("Storage is not dense")?.into_blob())
    }
}

impl PartialEq for Tensor {
    fn eq(&self, other: &Tensor) -> bool {
        if self.dt != other.dt || self.shape != other.shape {
            return false;
        }
        self.eq_dt(other).unwrap_or(false)
    }
}

impl Eq for Tensor {}

impl fmt::Debug for Tensor {
    fn fmt(&self, formatter: &mut fmt::Formatter) -> fmt::Result {
        let content = self.dump(false).unwrap_or_else(|e| format!("Error : {e:?}"));
        write!(formatter, "{content}")
    }
}

#[cfg(feature = "complex")]
pub fn reinterpret_inner_dim_as_complex(mut t: Tensor) -> TractResult<Tensor> {
    ensure!(
        t.shape().last() == Some(&2),
        "The last dimension in the tensor shape {:?} must be 2",
        t.shape()
    );
    unsafe {
        t.shape.pop();
        t.set_datum_type(t.datum_type().complexify()?);
        t.update_strides_and_len();
        Ok(t)
    }
}

#[cfg(feature = "complex")]
pub fn reinterpret_complex_as_inner_dim(mut t: Tensor) -> TractResult<Tensor> {
    unsafe {
        t.shape.push(2);
        t.set_datum_type(t.datum_type().decomplexify()?);
        t.update_strides_and_len();
        Ok(t)
    }
}

pub fn clip_range_bounds(len: usize, range: impl std::ops::RangeBounds<usize>) -> Range<usize> {
    use std::ops::Bound;
    let start = match range.start_bound() {
        Bound::Included(ix) => *ix,
        Bound::Excluded(ix) => ix + 1,
        Bound::Unbounded => 0,
    };
    let end = match range.end_bound() {
        Bound::Included(ix) => *ix + 1,
        Bound::Excluded(ix) => *ix,
        Bound::Unbounded => len,
    };
    start..end
}

pub fn natural_strides(shape: &[usize]) -> TVec<isize> {
    let mut strides = tvec!();
    compute_natural_stride_to(&mut strides, shape);
    strides
}

fn compute_natural_stride_to(strides: &mut TVec<isize>, shape: &[usize]) {
    match shape.len() {
        0 => (),
        1 => strides.push(1),
        2 => strides.extend_from_slice(&[shape[1] as isize, 1]),
        3 => strides.extend_from_slice(&[(shape[1] * shape[2]) as isize, shape[2] as _, 1]),
        4 => strides.extend_from_slice(&[
            (shape[1] * shape[2] * shape[3]) as isize,
            (shape[2] * shape[3]) as _,
            shape[3] as _,
            1,
        ]),
        _ => {
            strides.push(1);
            for dim in shape.as_ref().iter().skip(1).rev() {
                let previous = *strides.last().unwrap();
                strides.push(previous * *dim as isize)
            }
            strides.reverse();
        }
    }
}

impl<D: ::ndarray::Dimension, T: Datum> From<Array<T, D>> for Tensor {
    fn from(it: Array<T, D>) -> Tensor {
        Tensor::from_datum(it.into_dyn())
    }
}

/// Convenient conversion to Tensor.
pub trait IntoTensor: Sized {
    /// Convert Self to a Tensor.
    ///
    /// May perform a copy
    fn into_tensor(self) -> Tensor;
}

/// Convenient conversion to Arc<Tensor>.
pub trait IntoArcTensor: Sized {
    /// Convert Self to a Arc<Tensor>.
    ///
    /// May perform a copy
    fn into_arc_tensor(self) -> Arc<Tensor>;
}

impl<D: ::ndarray::Dimension, T: Datum> IntoTensor for Array<T, D> {
    fn into_tensor(self) -> Tensor {
        Tensor::from(self)
    }
}

impl<D: ::ndarray::Dimension, T: Datum> IntoArcTensor for Array<T, D> {
    fn into_arc_tensor(self) -> Arc<Tensor> {
        Arc::new(Tensor::from(self))
    }
}

impl IntoTensor for Tensor {
    fn into_tensor(self) -> Tensor {
        self
    }
}

impl IntoTensor for Arc<Tensor> {
    fn into_tensor(self) -> Tensor {
        Arc::try_unwrap(self).unwrap_or_else(|t| (*t).clone())
    }
}

impl IntoArcTensor for Tensor {
    fn into_arc_tensor(self) -> Arc<Tensor> {
        Arc::new(self)
    }
}

impl IntoArcTensor for Arc<Tensor> {
    fn into_arc_tensor(self) -> Arc<Tensor> {
        self
    }
}

#[cfg(test)]
mod tests {
    use crate::dim::SymbolScope;
    use crate::prelude::tensor1;

    use super::*;
    use litteral::tensor0;
    use proptest::collection::vec;
    use proptest::prelude::*;

    #[derive(Debug)]
    struct PermuteAxisProblem {
        shape: Vec<usize>,
        permutation: Vec<usize>,
    }

    impl Arbitrary for PermuteAxisProblem {
        type Strategy = BoxedStrategy<PermuteAxisProblem>;
        type Parameters = ();

        fn arbitrary_with(_: Self::Parameters) -> Self::Strategy {
            (0..8usize)
                .prop_flat_map(|rank| {
                    let permute: Vec<usize> = (0..rank).collect();
                    (proptest::collection::vec(1..5usize, rank), Just(permute).prop_shuffle())
                })
                .prop_map(|(shape, permutation)| PermuteAxisProblem { shape, permutation })
                .boxed()
        }
    }

    impl PermuteAxisProblem {
        fn input(&self) -> ArrayD<i32> {
            let mut i = 0;
            ArrayD::from_shape_simple_fn(&*self.shape, || {
                i += 1;
                i
            })
            .permuted_axes(&*self.permutation)
        }

        fn reference(&self) -> Tensor {
            let values: Vec<i32> = self.input().iter().copied().collect();
            let shape = self.permutation.iter().map(|ix| self.shape[*ix]).collect::<TVec<usize>>();
            super::litteral::tensor1(&values).into_shape(&shape).unwrap()
        }

        fn tract(&self) -> Tensor {
            Tensor::from(self.input())
        }

        fn check(&self) -> proptest::test_runner::TestCaseResult {
            prop_assert_eq!(self.tract(), self.reference());
            Ok(())
        }
    }

    proptest::proptest! {
        #[test]
        fn prop(pb: PermuteAxisProblem) {
            pb.check().unwrap();
        }
    }

    #[test]
    fn t_1_2() {
        PermuteAxisProblem { shape: vec![2, 1], permutation: vec![1, 0] }.check().unwrap();
    }

    #[test]
    fn t_2_2() {
        PermuteAxisProblem { shape: vec![2, 2], permutation: vec![1, 0] }.check().unwrap();
    }

    #[derive(Debug)]
    struct BroadcastVecToShape {
        vec: Vec<f32>,
        axis: usize,
        shape: TVec<usize>,
    }

    impl BroadcastVecToShape {
        fn check(&self) -> proptest::test_runner::TestCaseResult {
            let input = tensor1(&self.vec);
            let mut intermediate = tvec![1usize; self.shape.len()];
            intermediate[self.axis] = self.vec.len();
            let reference = input
                .clone()
                .into_shape(&intermediate)
                .unwrap()
                .broadcast_to_shape(&self.shape)
                .unwrap();
            prop_assert_eq!(
                reference,
                input.broadcast_vector_to_shape(&self.shape, self.axis).unwrap()
            );
            Ok(())
        }
    }

    impl Arbitrary for BroadcastVecToShape {
        type Strategy = BoxedStrategy<BroadcastVecToShape>;
        type Parameters = ();

        fn arbitrary_with(_: Self::Parameters) -> Self::Strategy {
            vec(0usize..5, 0usize..4)
                .prop_flat_map(|shape| {
                    (vec(-10f32..10f32, 0usize..5), Just(shape.clone()), 0..shape.len() + 1)
                })
                .prop_map(|(vec, mut shape, axis)| {
                    shape.insert(axis, vec.len());
                    BroadcastVecToShape { vec, shape: shape.into(), axis }
                })
                .boxed()
        }
    }

    proptest::proptest! {
        #[test]
        fn broadcast_vector_to_shape_prop(pb: BroadcastVecToShape) {
            pb.check().unwrap()
        }
    }

    #[test]
    #[cfg(feature = "complex")]
    fn test_reinterpret_inner_dim_as_complex() -> TractResult<()> {
        let input = crate::internal::tensor2(&[[1.0f32, 2.0], [3.0, 4.0], [5.0, 6.0]]);
        let cplx_input = reinterpret_inner_dim_as_complex(input)?;
        let expected = crate::internal::tensor1(&[
            Complex::new(1.0f32, 2.0),
            Complex::new(3.0, 4.0),
            Complex::new(5.0, 6.0),
        ]);
        assert_eq!(expected, cplx_input);
        Ok(())
    }

    #[test]
    #[cfg(feature = "complex")]
    fn test_reinterpret_inner_dim_as_complex_2() -> TractResult<()> {
        let input =
            crate::internal::tensor3(&[[[1i32, 2], [1, 2]], [[3, 4], [3, 4]], [[5, 6], [5, 6]]]);
        let cplx_input = reinterpret_inner_dim_as_complex(input)?;
        let expected = crate::internal::tensor2(&[
            [Complex::new(1i32, 2), Complex::new(1, 2)],
            [Complex::new(3, 4), Complex::new(3, 4)],
            [Complex::new(5, 6), Complex::new(5, 6)],
        ]);
        assert_eq!(expected, cplx_input);
        Ok(())
    }

    #[test]
    fn clone_tdim_tensor() {
        let symbols = SymbolScope::default();
        let a = symbols.sym("a");
        let t = tensor0(TDim::from(a));
        let _ = t.clone();
    }
}